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MySQL Optimization, part 1

While optimization is possible with limited knowledge of your system or application, the more you know about your system, the better your optimization will be. This article, the first of two parts, covers some of the different points you will need to know for optimizing MySQL. It is excerpted from chapter six of the book MySQL Administrator’s Guide, by MySQL AB (Sams, 2004; ISBN: 0672326345)

Optimization is a complex task because ultimately it requires understanding
of the entire system to be optimized. Although it may be possible to perform
some local optimizations with little knowledge of your system or application,
the more optimal you want your system to become, the more you will have to know
about it.

This chapter tries to explain and give some examples of different ways to
optimize MySQL. Remember, however, that there are always additional ways to make
the system even faster, although they may require increasing effort to
achieve.

6.1 Optimization Overview

The most important factor in making a system fast is its basic design. You
also need to know what kinds of things your system will be doing, and what your
bottlenecks are.

The most common system bottlenecks are:

Disk seeks. It takes time for the disk to find a piece of data. With modern
disks, the mean time for this is usually lower than 10ms, so we can in theory do
about 100 seeks a second. This time improves slowly with new disks and is very
hard to optimize for a single table. The way to optimize seek time is to
distribute the data onto more than one disk.

Disk reading and writing. When the disk is at the correct position, we need
to read the data. With modern disks, one disk delivers at least 10-20MB/s
throughput. This is easier to optimize than seeks because you can read in
parallel from multiple disks.

CPU cycles. When we have the data in main memory (or if it was already
there), we need to process it to get our result. Having small tables compared to
the amount of memory is the most common limiting factor. But with small tables,
speed is usually not the problem.

Memory bandwidth. When the CPU needs more data than can fit in the CPU cache,
main memory bandwidth becomes a bottleneck. This is an uncommon bottleneck for
most systems, but one to be aware of.

6.1.1 MySQL Design Limitations and Tradeoffs

When using the MyISAM storage engine, MySQL uses extremely fast
table locking that allows multiple readers or a single writer. The biggest
problem with this storage engine occurs when you have a steady stream of mixed
updates and slow selects on a single table. If this is a problem for certain
tables, you can use another table type for them. See Chapter 8, “MySQL Storage
Engines and Table Types.”

MySQL can work with both transactional and non-transactional tables. To be
able to work smoothly with non-transactional tables (which can’t roll back if
something goes wrong), MySQL has the following rules:

All columns have default values.

If you insert a “wrong” value in a column, such as a too-large numerical
value into a numerical column, MySQL sets the column to the “best possible
value” instead of giving an error. For numerical values, this is 0, the smallest
possible value, or the largest possible value. For strings, this is either the
empty string or the longest possible string that can be stored in the
column.

All calculated expressions return a value that can be used instead of
signaling an error condition. For example, 1/0 returns
NULL.

The implication of these rules is that you should not use MySQL to check
column content. Instead, you should check values within your application before
storing them in the database.

6.1.2 Designing Applications for Portability

Because all SQL servers implement different parts of standard SQL, it takes
work to write portable SQL applications. It is very easy to achieve portability
for very simple selects and inserts, but becomes more difficult the more
capabilities you require. If you want an application that is fast with many
database systems, it becomes even harder!

To make a complex application portable, you need to determine which SQL
servers it must work with, then determine what features those servers
support.

All database systems have some weak points. That is, they have different
design compromises that lead to different behavior.

You can use the MySQL crash-me program to find functions, types, and
limits that you can use with a selection of database servers. crash-me
does not check for every possible feature, but it is still reasonably
comprehensive, performing about 450 tests.

An example of the type of information crash-me can provide is that
you shouldn’t have column names longer than 18 characters if you want to be able
to use Informix or DB2.

The crash-me program and the MySQL benchmarks are all very database
independent. By taking a look at how they are written, you can get a feeling for
what you have to do to make your own applications database independent. The
programs can be found in the sql-bench directory of MySQL source
distributions. They are written in Perl and use the DBI database interface. Use
of DBI in itself solves part of the portability problem because it provides
database-independent access methods.

If you strive for database independence, you need to get a good feeling for
each SQL server’s bottlenecks. For example, MySQL is very fast in retrieving and
updating records for MyISAM tables, but will have a problem in mixing
slow readers and writers on the same table. Oracle, on the other hand, has a big
problem when you try to access rows that you have recently updated (until they
are flushed to disk). Transactional databases in general are not very good at
generating summary tables from log tables, because in this case row locking is
almost useless.

To make your application really database independent, you need to
define an easily extendable interface through which you manipulate your data.
Because C++ is available on most systems, it makes sense to use a C++
class-based interface to the databases.

If you use some feature that is specific to a given database system (such as
the REPLACE statement, which is specific to MySQL), you should
implement the same feature for other SQL servers by coding an alternative
method. Although the alternative may be slower, it will allow the other servers
to perform the same tasks.

With MySQL, you can use the /*! */ syntax to add MySQL-specific
keywords to a query. The code inside /**/ will be treated as a comment
(and ignored) by most other SQL servers.

If high performance is more important than exactness, as in some Web
applications, it is possible to create an application layer that caches all
results to give you even higher performance. By letting old results “expire”
after a while, you can keep the cache reasonably fresh. This provides a method
to handle high load spikes, in which case you can dynamically increase the cache
and set the expiration timeout higher until things get back to normal.

In this case, the table creation information should contain information of
the initial size of the cache and how often the table should normally be
refreshed.

An alternative to implementing an application cache is to use the MySQL query
cache. By enabling the query cache, the server handles the details of
determining whether a query result can be reused. This simplifies your
application. See Section 4.10, “The MySQL Query Cache.”

6.1.3 What We Have Used MySQL For

This section describes an early application for MySQL.

During MySQL initial development, the features of MySQL were made to fit our
largest customer, which handled data warehousing for a couple of the largest
retailers in Sweden.

From all stores, we got weekly summaries of all bonus card transactions, and
were expected to provide useful information for the store owners to help them
find how their advertising campaigns were affecting their own customers.

The volume of data was quite huge (about seven million summary transactions
per month), and we had data for 4-10 years that we needed to present to the
users. We got weekly requests from our customers, who wanted to get “instant”
access to new reports from this data.

We solved this problem by storing all information per month in compressed
“transaction” tables. We had a set of simple macros that generated summary
tables grouped by different criteria (product group, customer id, store, and so
on) from the tables in which the transactions were stored. The reports were Web
pages that were dynamically generated by a small Perl script. This script parsed
a Web page, executed the SQL statements in it, and inserted the results. We
would have used PHP or mod_perl instead, but they were not available at the
time.

For graphical data, we wrote a simple tool in C that could process SQL query
results and produce GIF images based on those results. This tool also was
dynamically executed from the Perl script that parsed the Web pages.

In most cases, a new report could be created simply by copying an existing
script and modifying the SQL query in it. In some cases, we needed to add more
columns to an existing summary table or generate a new one, but this also was
quite simple because we kept all transaction-storage tables on disk. (This
amounted to about 50GB of transaction tables and 200GB of other customer
data.)

We also let our customers access the summary tables directly with ODBC so
that the advanced users could experiment with the data themselves.

This system worked well and we had no problems handling the data with quite
modest Sun Ultra SPARCstation hardware (2x200MHz). Eventually the system was
migrated to Linux.

This article is excerpted from MySQL
Administrator’s Guide, by MySQL AB (editor) (Sams, 2004;
ISBN 0672326345). Check it out at your
favorite bookstore today. Buy
this book now.

{mospagebreak title=6.1.4 The MySQL Benchmark Suite}

This section should contain a technical description of the MySQL benchmark
suite (and crash-me), but that description has not yet been written.
Currently, you can get a good idea of the benchmarks by looking at the code and
results in the sql-bench directory in any MySQL source
distribution.

This benchmark suite is meant to tell any user what operations a given SQL
implementation performs well or poorly.

Note that this benchmark is single-threaded, so it measures the minimum time
for the operations performed. We plan to add multi-threaded tests to the
benchmark suite in the future.

To use the benchmark suite, the following requirements must be satisfied:

The benchmark suite is provided with MySQL source distributions. You can
either download a released distribution from http://dev.mysql.com/downloads/, or use the current
development source tree (see Section 2.3.3, “Installing from the Development
Source Tree”).

The benchmark scripts are written in Perl and use the Perl DBI module to
access database servers, so DBI must be installed. You will also need the
server-specific DBD drivers for each of the servers you want to test. For
example, to test MySQL, PostgreSQL, and DB2, you must have the
DBD::mysql, DBD::Pg, and DBD::DB2 modules installed.
See Section 2.7, “Perl Installation Notes.”

After you obtain a MySQL source distribution, you will find the benchmark
suite in its sql-bench directory. To run the benchmark tests, build
MySQL, then change location into the sql-bench directory and execute
the run-all-tests script:

shell> cd sql-bench
shell> perl run-all-tests –server=server_name

server_name is one of the supported servers. To get a list of
all options and supported servers, invoke this command:

shell> perl run-all-tests –help

The crash-me script also is located in the sql-bench
directory. crash-me tries to determine what features a database
supports and what its capabilities and limitations are by actually running
queries. For example, it determines:

You should definitely benchmark your application and database to find out
where the bottlenecks are. By fixing a bottleneck (or by replacing it with a
“dummy module”), you can then easily identify the next bottleneck. Even if the
overall performance for your application currently is acceptable, you should at
least make a plan for each bottleneck, and decide how to solve it if someday you
really need the extra performance.

For an example of portable benchmark programs, look at the MySQL benchmark
suite. See Section 6.1.4, “The MySQL Benchmark Suite.” You can take any program
from this suite and modify it for your needs. By doing this, you can try
different solutions to your problem and test which really is fastest for
you.

It is very common for a problem to occur only when the system is very heavily
loaded. We have had many customers who contact us when they have a (tested)
system in production and have encountered load problems. In most cases,
performance problems turn out to be due to issues of basic database design (for
example, table scans are not good at high load) or problems with the
operating system or libraries. Most of the time, these problems would be a
lot easier to fix if the systems were not already in production.

To avoid problems like this, you should put some effort into benchmarking
your whole application under the worst possible load! You can use Super Smack
for this. It is available at http://jeremy.zawodny.com/mysql/super-smack/. As the name
suggests, it can bring a system to its knees if you ask it, so make sure to use
it only on your development systems.

6.2 Optimizing SELECT Statements and Other
Queries

First, one factor affects all statements: The more complex your permission
setup is, the more overhead you will have.

Using simpler permissions when you issue GRANT statements enables
MySQL to reduce permission-checking overhead when clients execute statements.
For example, if you don’t grant any table-level or column-level privileges, the
server need not ever check the contents of the tables_priv and
columns_priv tables. Similarly, if you place no resource limits on any
accounts, the server does not have to perform resource counting. If you have a
very high query volume, it may be worth the time to use a simplified grant
structure to reduce permission-checking overhead.

If your problem is with some specific MySQL expression or function, you can
use the BENCHMARK() function from the mysql client program to
perform a timing test. Its syntax is
BENCHMARK(loop_count,expression). For example:

The EXPLAIN statement can be used either as a synonym for
DESCRIBE or as a way to obtain information about how MySQL will execute
a SELECT statement:

The EXPLAIN tbl_name syntax is synonymous with DESCRIBE
tbl_name or SHOW COLUMNS FROM tbl_name.

When you precede a SELECT statement with the keyword
EXPLAIN, MySQL explains how it would process the SELECT,
providing information about how tables are joined and in which
order.

This section provides information about the second use of
EXPLAIN.

With the help of EXPLAIN, you can see when you must add indexes to
tables to get a faster SELECT that uses indexes to find records.

You should frequently run ANALYZE TABLE to update table statistics
such as cardinality of keys, which can affect the choices the optimizer
makes.

You can also see whether the optimizer joins the tables in an optimal order.
To force the optimizer to use a join order corresponding to the order in which
the tables are named in the SELECT statement, begin the statement with
SELECT STRAIGHT_JOIN rather than just SELECT.

EXPLAIN returns a row of information for each table used in the
SELECT statement. The tables are listed in the output in the order that
MySQL would read them while processing the query. MySQL resolves all joins using
a single-sweep multi-join method. This means that MySQL reads a row from the
first table, then finds a matching row in the second table, then in the third
table, and so on. When all tables are processed, it outputs the selected columns
and backtracks through the table list until a table is found for which there are
more matching rows. The next row is read from this table and the process
continues with the next table.

In MySQL version 4.1, the EXPLAIN output format was changed to work
better with constructs such as UNION statements, subqueries, and
derived tables. Most notable is the addition of two new columns: id and
select_type. You will not see these columns when using servers older
than MySQL 4.1.

Each output row from EXPLAIN provides information about one table,
and each row consists of the following columns:

id

The SELECT identifier. This is the sequential number of the
SELECT within the query.

select_type

The type of SELECT, which can be any of the following:

SIMPLE

Simple SELECT (not using UNION or subqueries)

PRIMARY

Outermost SELECT

UNION

Second or later SELECT statement in a UNION

DEPENDENT UNION

Second or later SELECT statement in a UNION, dependent on
outer subquery

SUBQUERY

First SELECT in subquery

DEPENDENT SUBQUERY

First SELECT in subquery, dependent on outer subquery

DERIVED

Derived table SELECT (subquery in FROM
clause)

table

The table to which the row of output refers.

type

The join type. The different join types are listed here, ordered from the
best type to the worst:

system

The table has only one row (= system table). This is a special case of the
const join type.

const

The table has at most one matching row, which will be read at the start of
the query. Because there is only one row, values from the column in this row can
be regarded as constants by the rest of the optimizer. const tables are
very fast because they are read only once!

const is used when you compare all parts of a PRIMARY KEY
or UNIQUE index with constant values. In the following queries,
tbl_name can be used as a const table:

SELECT * FROM tbl_name WHERE primary_key=1;
SELECT * FROM tbl_name
WHERE primary_key_part1=1 AND primary_key_part2=2;

eq_ref

One row will be read from this table for each combination of rows from the
previous tables. Other than the const types, this is the best possible
join type. It is used when all parts of an index are used by the join and the
index is a PRIMARY KEY or UNIQUE index.

eq_ref can be used for indexed columns that are compared using the
= operator. The comparison value can be a constant or an expression
that uses columns from tables that are read before this table.

In the following examples, MySQL can use an eq_ref join to process
ref_table:

SELECT * FROM ref_table,other_table
WHERE ref_table.key_column=other_table.column;
SELECT * FROM ref_table,other_table
WHERE ref_table.key_column_part1=other_table.column
AND ref_table.key_column_part2=1;

ref

All rows with matching index values will be read from this table for each
combination of rows from the previous tables. ref is used if the join
uses only a leftmost prefix of the key or if the key is not a PRIMARY
KEY or UNIQUE index (in other words, if the join cannot select a
single row based on the key value). If the key that is used matches only a few
rows, this is a good join type.

ref can be used for indexed columns that are compared using the
= operator.

In the following examples, MySQL can use a ref join to process
ref_table:

SELECT * FROM ref_table WHERE key_column=expr;
SELECT * FROM ref_table,other_table
WHERE ref_table.key_column=other_table.column;
SELECT * FROM ref_table,other_table
WHERE ref_table.key_column_part1=other_table.column
AND ref_table.key_column_part2=1;

ref_or_null

This join type is like ref, but with the addition that MySQL will do
an extra search for rows that contain NULL values. This join type
optimization is new for MySQL 4.1.1 and is mostly used when resolving
subqueries.

In the following examples, MySQL can use a ref_or_null join to
process ref_table:

SELECT * FROM ref_table
WHERE key_column=expr OR key_column IS NULL;

See Section 6.2.6, “How MySQL Optimizes IS NULL.”

index_merge

This join type indicates that the Index Merge optimization is used.
In this case, the key column contains a list of indexes used, and
key_len contains a list of the longest key parts for the indexes used.
For more information, see Section 6.2.5, “How MySQL Optimizes OR
Clauses.”

unique_subquery

This type replaces ref for some IN subqueries of the
following form:

value IN (SELECT primary_key FROM single_table WHERE some_expr)

unique_subquery is just an index lookup function that replaces the
subquery completely for better efficiency.

index_subquery

This join type is similar to unique_subquery. It replaces
IN subqueries, but it works for non-unique indexes in subqueries of the
following form:

value IN (SELECT key_column FROM single_table WHERE some_expr)

range

Only rows that are in a given range will be retrieved, using an index to
select the rows. The key column indicates which index is used. The
key_len contains the longest key part that was used. The ref
column will be NULL for this type.

range can be used for when a key column is compared to a constant
using any of the =, <>, >, >=,
<, <=, IS NULL, <=>,
BETWEEN, or IN operators:

SELECT * FROM tbl_name
WHERE key_column = 10;
SELECT * FROM tbl_name
WHERE key_column BETWEEN 10 and 20;
SELECT * FROM tbl_name
WHERE key_column IN (10,20,30);
SELECT * FROM tbl_name
WHERE key_part1= 10 AND key_part2 IN (10,20,30);

index

This join type is the same as ALL, except that only the index tree
is scanned. This usually is faster than ALL, because the index file
usually is smaller than the data file.

MySQL can use this join type when the query uses only columns that are part
of a single index.

ALL

A full table scan will be done for each combination of rows from the previous
tables. This is normally not good if the table is the first table not marked
const, and usually very bad in all other cases. Normally, you
can avoid ALL by adding indexes that allow row retrieval from the table
based on constant values or column values from earlier tables.

possible_keys

The possible_keys column indicates which indexes MySQL could use to
find the rows in this table. Note that this column is totally independent of the
order of the tables as displayed in the output from EXPLAIN. That means
that some of the keys in possible_keys might not be usable in practice
with the generated table order.

If this column is NULL, there are no relevant indexes. In this case,
you may be able to improve the performance of your query by examining the
WHERE clause to see whether it refers to some column or columns that
would be suitable for indexing. If so, create an appropriate index and check the
query with EXPLAIN again.

To see what indexes a table has, use SHOW INDEX FROM
tbl_name.

key

The key column indicates the key (index) that MySQL actually decided
to use. The key is NULL if no index was chosen. To force MySQL to use
or ignore an index listed in the possible_keys column, use FORCE
INDEX, USE INDEX, or IGNORE INDEX in your query.

For MyISAM and BDB tables, running ANALYZE TABLE
will help the optimizer choose better indexes. For MyISAM tables,
myisamchk –analyze will do the same. See Section 4.6.2, “Table
Maintenance and Crash Recovery.”

key_len

The key_len column indicates the length of the key that MySQL
decided to use. The length is NULL if the key column says
NULL. Note that the value of key_len allows you to determine
how many parts of a multiple-part key MySQL will actually use.

ref

The ref column shows which columns or constants are used with the
key to select rows from the table.

rows

The rows column indicates the number of rows MySQL believes it must
examine to execute the query.

Extra

This column contains additional information about how MySQL will resolve the
query. Here is an explanation of the different text strings that can appear in
this column:

Distinct

MySQL will stop searching for more rows for the current row combination after
it has found the first matching row.

Not exists

MySQL was able to do a LEFT JOIN optimization on the query and will
not examine more rows in this table for the previous row combination after it
finds one row that matches the LEFT JOIN criteria.

Here is an example of the type of query that can be optimized this way:

SELECT * FROM t1 LEFT JOIN t2 ON t1.id=t2.id
WHERE t2.id IS NULL;

Assume that t2.id is defined as NOT NULL. In this case,
MySQL will scan t1 and look up the rows in t2 using the values
of t1.id. If MySQL finds a matching row in t2, it knows that
t2.id can never be NULL, and will not scan through the rest of
the rows in t2 that have the same id value. In other words,
for each row in t1, MySQL needs to do only a single lookup in
t2, regardless of how many rows actually match in t2.

range checked for each record (index map: #)

MySQL found no good index to use. Instead, for each row combination in the
preceding tables, it will do a check to determine which index to use (if any),
and use it to retrieve the rows from the table. This is not very fast, but is
faster than performing a join with no index at all.

Using filesort

MySQL will need to do an extra pass to find out how to retrieve the rows in
sorted order. The sort is done by going through all rows according to the join
type and storing the sort key and pointer to the row for all rows that match the
WHERE clause. The keys then are sorted and the rows are retrieved in
sorted order.

Using index

The column information is retrieved from the table using only information in
the index tree without having to do an additional seek to read the actual row.
This strategy can be used when the query uses only columns that are part of a
single index.

Using temporary

To resolve the query, MySQL will need to create a temporary table to hold the
result. This typically happens if the query contains GROUP BY and
ORDER BY clauses that list columns differently.

Using where

A WHERE clause will be used to restrict which rows to match against
the next table or send to the client. Unless you specifically intend to fetch or
examine all rows from the table, you may have something wrong in your query if
the Extra value is not Using where and the table join type is
ALL or index.

If you want to make your queries as fast as possible, you should look out for
Extra values of Using filesort and Using
temporary.

You can get a good indication of how good a join is by taking the product of
the values in the rows column of the EXPLAIN output. This
should tell you roughly how many rows MySQL must examine to execute the query.
If you restrict queries with the max_join_size system variable, this
product also is used to determine which multiple-table SELECT
statements to execute. See Section 6.5.2, “Tuning Server Parameters.”

The following example shows how a multiple-table join can be optimized
progressively based on the information provided by EXPLAIN.

Suppose that you have the SELECT statement shown here and you plan
to examine it using EXPLAIN:

Because type is ALL for each table, this output indicates
that MySQL is generating a Cartesian product of all the tables; that is, every
combination of rows. This will take quite a long time, because the product of
the number of rows in each table must be examined. For the case at hand, this
product is 74 * 2135 * 74 * 3872 = 45,268,558,720 rows. If the tables
were bigger, you can only imagine how long it would take.

One problem here is that MySQL can use indexes on columns more efficiently if
they are declared the same. (For ISAM tables, indexes may not be used
at all unless the columns are declared the same.) In this context,
VARCHAR and CHAR are the same unless they are declared as
different lengths. Because tt.ActualPC is declared as CHAR(10)
and et.EMPLOYID is declared as CHAR(15), there is a length
mismatch.

To fix this disparity between column lengths, use ALTER TABLE to
lengthen ActualPC from 10 characters to 15 characters:

mysql> ALTER TABLE tt MODIFY ActualPC VARCHAR(15);

Now tt.ActualPC and et.EMPLOYID are both
VARCHAR(15). Executing the EXPLAIN statement again produces
this result:

The remaining problem is that, by default, MySQL assumes that values in the
tt.ActualPC column are evenly distributed, and that is not the case for
the tt table. Fortunately, it is easy to tell MySQL to analyze the key
distribution:

Note that the rows column in the output from EXPLAIN is an
educated guess from the MySQL join optimizer. You should check whether the
numbers are even close to the truth. If not, you may get better performance by
using STRAIGHT_JOIN in your SELECT statement and trying to
list the tables in a different order in the FROM clause.

This article is excerpted from MySQL
Administrator’s Guide, by MySQL AB (editor) (Sams, 2004;
ISBN 0672326345). Check it out at your
favorite bookstore today. Buy
this book now.

{mospagebreak title=6.2.2 Estimating Query Performance}

In most cases, you can estimate the performance by counting disk seeks. For
small tables, you can usually find a row in one disk seek (because the index is
probably cached). For bigger tables, you can estimate that, using B-tree
indexes, you will need this many seeks to find a row:
log(row_count) / log(index_block_length / 3 * 2 /
(index_length + data_pointer_length)) + 1.

In MySQL, an index block is usually 1024 bytes and the data pointer is
usually 4 bytes. For a 500,000-row table with an index length of 3 bytes (medium
integer), the formula indicates log(500,000)/log(1024/3*2/(3+4)) + 1 =
4 seeks.

This index would require storage of about 500,000 * 7 * 3/2 = 5.2MB (assuming
a typical index buffer fill ratio of 2/3), so you will probably have much of the
index in memory and you will probably need only one or two calls to read data to
find the row.

For writes, however, you will need four seek requests (as above) to find
where to place the new index and normally two seeks to update the index and
write the row.

Note that the preceding discussion doesn’t mean that your application
performance will slowly degenerate by log N! As long as
everything is cached by the OS or SQL server, things will become only marginally
slower as the table gets bigger. After the data gets too big to be cached,
things will start to go much slower until your application is only bound by
disk-seeks (which increase by log N). To avoid this, increase
the key cache size as the data grows. For MyISAM tables, the key cache
size is controlled by the key_buffer_size system variable. See Section
6.5.2, “Tuning Server Parameters.”

6.2.3 Speed of SELECT Queries

In general, when you want to make a slow SELECT … WHERE query
faster, the first thing to check is whether you can add an index. All references
between different tables should usually be done with indexes. You can use the
EXPLAIN statement to determine which indexes are used for a
SELECT. See Section 6.4.5, “How MySQL Uses Indexes,” and Section 6.2.1,
“EXPLAIN Syntax (Get Information About a SELECT).”

Some general tips for speeding up queries on MyISAM tables:

To help MySQL optimize queries better, use ANALYZE TABLE or run
myisamchk –analyze on a table after it has been loaded with data. This
updates a value for each index part that indicates the average number of rows
that have the same value. (For unique indexes, this is always 1.) MySQL will use
this to decide which index to choose when you join two tables based on a
non-constant expression. You can check the result from the table analysis by
using SHOW INDEX FROM tbl_name and examining the
Cardinality value. myisamchk –description –verbose shows
index distribution information.

To sort an index and data according to an index, use myisamchk
–sort-index –sort-records=1 (if you want to sort on index 1). This is a
good way to make queries faster if you have a unique index from which you want
to read all records in order according to the index. Note that the first time
you sort a large table this way, it may take a long time.

6.2.4 How MySQL Optimizes WHERE Clauses

This section discusses optimizations that can be made for processing
WHERE clauses. The examples use SELECT statements, but the
same optimizations apply for WHERE clauses in DELETE and
UPDATE statements.

Note that work on the MySQL optimizer is ongoing, so this section is
incomplete. MySQL does many optimizations, not all of which are documented
here.

Some of the optimizations performed by MySQL are listed here:

Removal of unnecessary parentheses:

((a AND b) AND c OR (((a AND b) AND (c AND d))))
-> (a AND b AND c) OR (a AND b AND c AND d)

Constant folding:

(a<b AND b=c) AND a=5
-> b>5 AND b=c AND a=5

Constant condition removal (needed because of constant folding):

(B>=5 AND B=5) OR (B=6 AND 5=5) OR (B=7 AND 5=6)
-> B=5 OR B=6

Constant expressions used by indexes are evaluated only once.

COUNT(*) on a single table without a WHERE is retrieved
directly from the table information for MyISAM and HEAP
tables. This is also done for any NOT NULL expression when used with
only one table.

Early detection of invalid constant expressions. MySQL quickly detects that
some SELECT statements are impossible and returns no rows.

HAVING is merged with WHERE if you don’t use GROUP
BY or group functions (COUNT(), MIN(), and so on).

For each table in a join, a simpler WHERE is constructed to get a
fast WHERE evaluation for the table and also to skip records as soon as
possible.

All constant tables are read first before any other tables in the query. A
constant table is any of the following:

An empty table or a table with one row.

A table that is used with a WHERE clause on a PRIMARY KEY
or a UNIQUE index, where all index parts are compared to constant
expressions and are defined as NOT NULL.

All of the following tables are used as constant tables:

SELECT * FROM t WHERE primary_key=1;
SELECT * FROM t1,t2
WHERE t1.primary_key=1 AND t2.primary_key=t1.id;

The best join combination for joining the tables is found by trying all
possibilities. If all columns in ORDER BY and GROUP BY clauses
come from the same table, that table is preferred first when joining.

If there is an ORDER BY clause and a different GROUP BY
clause, or if the ORDER BY or GROUP BY contains columns from
tables other than the first table in the join queue, a temporary table is
created.

If you use SQL_SMALL_RESULT, MySQL uses an in-memory temporary
table.

Each table index is queried, and the best index is used unless the optimizer
believes that it will be more efficient to use a table scan. At one time, a scan
was used based on whether the best index spanned more than 30% of the table. Now
the optimizer is more complex and bases its estimate on additional factors such
as table size, number of rows, and I/O block size, so a fixed percentage no
longer determines the choice between using an index or a scan.

In some cases, MySQL can read rows from the index without even consulting the
data file. If all columns used from the index are numeric, only the index tree
is used to resolve the query.

Before each record is output, those that do not match the HAVING
clause are skipped.

The following queries are resolved using only the index tree, assuming that
the indexed columns are numeric:

SELECT key_part1,key_part2 FROM tbl_name WHERE key_part1=val;
SELECT COUNT(*) FROM tbl_name
WHERE key_part1=val1 AND key_part2=val2;
SELECT key_part2 FROM tbl_name GROUP BY key_part1;

The following queries use indexing to retrieve the rows in sorted order
without a separate sorting pass:

SELECT … FROM tbl_name
ORDER BY key_part1,key_part2,… ;
SELECT … FROM tbl_name
ORDER BY key_part1 DESC, key_part2 DESC, … ;

6.2.5 How MySQL Optimizes OR Clauses

The Index Merge method is used to retrieve rows with several
ref, ref_or_null, or range scans and merge the
results into one. This method is employed when the table condition is a
disjunction of conditions for which ref, ref_or_null, or
range could be used with different keys.

This “join” type optimization is new in MySQL 5.0.0, and represents a
significant change in behavior with regard to indexes, because the old
rule was that the server is only ever able to use at most one index for each
referenced table.

In EXPLAIN output, this method appears as index_merge in
the type column. In this case, the key column contains a list
of indexes used, and key_len contains a list of the longest key parts
for those indexes.

Examples:

SELECT * FROM tbl_name WHERE key_part1 = 10 OR key_part2 = 20;
SELECT * FROM tbl_name
WHERE (key_part1 = 10 OR key_part2 = 20) AND non_key_part=30;
SELECT * FROM t1,t2
WHERE (t1.key1 IN (1,2) OR t1.key2 LIKE ‘value%’)
AND t2.key1=t1.some_col;
SELECT * FROM t1,t2
WHERE t1.key1=1
AND (t2.key1=t1.some_col OR t2.key2=t1.some_col2);

MySQL can do the same optimization on col_name IS NULL that
it can do with col_name = constant_value. For example,
MySQL can use indexes and ranges to search for NULL with IS
NULL.

SELECT * FROM tbl_name WHERE key_col IS NULL;
SELECT * FROM tbl_name WHERE key_col <=> NULL;
SELECT * FROM tbl_name
WHERE key_col=const1 OR key_col=const2 OR key_col IS NULL;

If a WHERE clause includes a col_name IS NULL
condition for a column that is declared as NOT NULL, that expression
will be optimized away. This optimization does not occur in cases when the
column might produce NULL anyway; for example, if it comes from a table
on the right side of a LEFT JOIN.

MySQL 4.1.1 and up can additionally optimize the combination
col_name = expr AND col_name IS NULL, a form that
is common in resolved subqueries. EXPLAIN will show
ref_or_null when this optimization is used.

This optimization can handle one IS NULL for any key part.

Some examples of queries that are optimized, assuming that there is an index
on columns a and b or table t2:

SELECT * FROM t1 WHERE t1.a=expr OR t1.a IS NULL;
SELECT * FROM t1,t2 WHERE t1.a=t2.a OR t2.a IS NULL;
SELECT * FROM t1,t2
WHERE (t1.a=t2.a OR t2.a IS NULL) AND t2.b=t1.b;
SELECT * FROM t1,t2
WHERE t1.a=t2.a AND (t2.b=t1.b OR t2.b IS NULL);
SELECT * FROM t1,t2
WHERE (t1.a=t2.a AND t2.a IS NULL AND …)
OR (t1.a=t2.a AND t2.a IS NULL AND …);

ref_or_null works by first doing a read on the reference key, and
then a separate search for rows with a NULL key value.

Note that the optimization can handle only one IS NULL level. In the
following query, MySQL will use key lookups only on the expression
(t1.a=t2.a AND t2.a IS NULL) and not be able to use the key part on
b:

SELECT * FROM t1,t2
WHERE (t1.a=t2.a AND t2.a IS NULL)
OR (t1.b=t2.b AND t2.b IS NULL) ;

6.2.7 How MySQL Optimizes DISTINCT

DISTINCT combined with ORDER BY will need a temporary table
in many cases.

Note that because DISTINCT may use GROUP BY, you should be
aware of how MySQL works with columns in ORDER BY or HAVING
clauses that are not part of the selected columns.

MySQL extends the use of GROUP BY so that you can use columns or
calculations in the SELECT list that don’t appear in the GROUP
BY clause. This stands for any possible value for this group. You
can use this to get better performance by avoiding sorting and grouping on
unnecessary items. For example, you don’t need to group on
customer.name in the following query:

In standard SQL, you would have to add customer.name to the
GROUP BY clause. In MySQL, the name is redundant if you don’t run in
ANSI mode.

Do not use this feature if the columns you omit from the GROUP
BY part are not unique in the group! You will get unpredictable
results.

In some cases, you can use MIN() and MAX() to obtain a
specific column value even if it isn’t unique. The following gives the value of
column from the row containing the smallest value in the sort
column:

SUBSTR(MIN(CONCAT(RPAD(sort,6,’ ‘),column)),7)

When combining LIMIT row_count with DISTINCT, MySQL
stops as soon as it finds row_count unique rows.

If you don’t use columns from all tables named in a query, MySQL stops
scanning the not-used tables as soon as it finds the first match. In the
following case, assuming that t1 is used before t2 (which you
can check with EXPLAIN), MySQL stops reading from t2 (for any
particular row in t1) when the first row in t2 is found:

SELECT DISTINCT t1.a FROM t1,t2 where t1.a=t2.a;

6.2.8 How MySQL Optimizes LEFT JOIN and RIGHT
JOIN

A LEFT JOIN B join_condition is implemented in MySQL as
follows:

Table B is set to depend on table A and all tables on which
A depends.

Table A is set to depend on all tables (except B) that are
used in the LEFT JOIN condition.

The LEFT JOIN condition is used to decide how to retrieve rows from
table B. (In other words, any condition in the WHERE clause is
not used.)

All standard join optimizations are done, with the exception that a table is
always read after all tables on which it depends. If there is a circular
dependence, MySQL issues an error.

All standard WHERE optimizations are done.

If there is a row in A that matches the WHERE clause, but
there is no row in B that matches the ON condition, an extra
B row is generated with all columns set to NULL.

If you use LEFT JOIN to find rows that don’t exist in some table and
you have the following test: col_name IS NULL in the
WHERE part, where col_name is a column that is declared
as NOT NULL, MySQL stops searching for more rows (for a particular key
combination) after it has found one row that matches the LEFT JOIN
condition.

RIGHT JOIN is implemented analogously to LEFT JOIN, with
the roles of the tables reversed.

The join optimizer calculates the order in which tables should be joined. The
table read order forced by LEFT JOIN and STRAIGHT_JOIN helps
the join optimizer do its work much more quickly, because there are fewer table
permutations to check. Note that this means that if you do a query of the
following type, MySQL will do a full scan on b because the LEFT
JOIN forces it to be read before d:

SELECT *
FROM a,b LEFT JOIN c ON (c.key=a.key) LEFT JOIN d ON (d.key=a.key)
WHERE b.key=d.key;

The fix in this case is to rewrite the query as follows:

SELECT *
FROM b,a LEFT JOIN c ON (c.key=a.key) LEFT JOIN d ON (d.key=a.key)
WHERE b.key=d.key;

Starting from 4.0.14, MySQL does the following LEFT JOIN
optimization: If the WHERE condition is always false for the generated
NULL row, the LEFT JOIN is changed to a normal join.

For example, the WHERE clause would be false in the following query
if t2.column1 would be NULL:

SELECT * FROM t1 LEFT JOIN t2 ON (column1) WHERE t2.column2=5;

Therefore, it’s safe to convert the query to a normal join:

SELECT * FROM t1,t2 WHERE t2.column2=5 AND t1.column1=t2.column1;

This can be made faster because MySQL can now use table t2 before
table t1 if this would result in a better query plan. To force a
specific table order, use STRAIGHT_JOIN.

This article is excerpted from MySQL
Administrator’s Guide, by MySQL AB (editor) (Sams, 2004;
ISBN 0672326345). Check it out at your
favorite bookstore today. Buy
this book now.

{mospagebreak title=6.2.9 How MySQL Optimizes ORDER BY}

In some cases, MySQL can use an index to satisfy an ORDER BY or
GROUP BY clause without doing any extra sorting.

The index can also be used even if the ORDER BY doesn’t match the
index exactly, as long as all the unused index parts and all the extra ORDER
BY columns are constants in the WHERE clause. The following
queries will use the index to resolve the ORDER BY or GROUP BY
part:

SELECT * FROM t1 ORDER BY key_part1,key_part2,… ;
SELECT * FROM t1 WHERE key_part1=constant ORDER BY key_part2;
SELECT * FROM t1 WHERE key_part1=constant GROUP BY key_part2;
SELECT * FROM t1 ORDER BY key_part1 DESC, key_part2 DESC;
SELECT * FROM t1
WHERE key_part1=1 ORDER BY key_part1 DESC, key_part2 DESC;

In some cases, MySQL cannot use indexes to resolve the ORDER
BY, although it still will use indexes to find the rows that match the
WHERE clause. These cases include the following:

You use ORDER BY on different keys:

SELECT * FROM t1 ORDER BY key1, key2;

You use ORDER BY on non-consecutive key parts:

SELECT * FROM t1 WHERE key_part2=constant ORDER BY key_part2;

You mix ASC and DESC:

SELECT * FROM t1 ORDER BY key_part1 DESC, key_part2 ASC;

The key used to fetch the rows is not the same as the one used in the
ORDER BY:

SELECT * FROM t1 WHERE key2=constant ORDER BY key1;

You are joining many tables, and the columns in the ORDER BY are not
all from the first non-constant table that is used to retrieve rows. (This is
the first table in the EXPLAIN output that doesn’t have a
const join type.)

You have different ORDER BY and GROUP BY expressions.

The type of table index used doesn’t store rows in order. For example, this
is true for a HASH index in a HEAP table.

In those cases where MySQL must sort the result, it uses the following
algorithm:

Read all rows according to key or by table scanning. Rows that don’t match
the WHERE clause are skipped.

Store the sort key value in a buffer. The size of the buffer is the value of
the sort_buffer_size system variable.

When the buffer gets full, run a qsort (quicksort) on it and store the result
in a temporary file. Save a pointer to the sorted block. (If all rows fit into
the sort buffer, no temporary file is created.)

Repeat the preceding steps until all rows have been read.

Do a multi-merge of up to MERGEBUFF (7) regions to one block in
another temporary file. Repeat until all blocks from the first file are in the
second file.

Repeat the following until there are fewer than MERGEBUFF2 (15)
blocks left.

On the last multi-merge, only the pointer to the row (the last part of the
sort key) is written to a result file.

Read the rows in sorted order by using the row pointers in the result file.
To optimize this, we read in a big block of row pointers, sort them, and use
them to read the rows in sorted order into a row buffer. The size of the buffer
is the value of the read_rnd_buffer_size system variable. The code for
this step is in the sql/records.cc source file.

With EXPLAIN SELECT … ORDER BY, you can check whether MySQL can
use indexes to resolve the query. It cannot if you see Using filesort
in the Extra column. See Section 6.2.1, “EXPLAIN Syntax (Get
Information About a SELECT).”

If you want to increase ORDER BY speed, first see whether you can
get MySQL to use indexes rather than an extra sorting phase. If this is not
possible, you can try the following strategies:

Increase the size of the sort_buffer_size variable.

Increase the size of the read_rnd_buffer_size variable.

Change tmpdir to point to a dedicated filesystem with lots of empty
space. If you use MySQL 4.1 or later, this option accepts several paths that are
used in round-robin fashion. Paths should be separated by colon characters
(‘:‘) on Unix and semicolon characters (‘;‘) on Windows,
NetWare, and OS/2. You can use this feature to spread the load across several
directories. Note: The paths should be for directories in filesystems
that are located on different physical disks, not different partitions of
the same disk.

By default, MySQL sorts all GROUP BY col1, col2, …
queries as if you specified ORDER BY col1, col2, … in
the query as well. If you include an ORDER BY clause explicitly that
contains the same column list, MySQL optimizes it away without any speed
penalty, although the sorting still occurs. If a query includes GROUP
BY but you want to avoid the overhead of sorting the result, you can
suppress sorting by specifying ORDER BY NULL. For example:

INSERT INTO foo
SELECT a, COUNT(*) FROM bar GROUP BY a ORDER BY NULL;

6.2.10 How MySQL Optimizes LIMIT

In some cases, MySQL will handle a query differently when you are using
LIMIT row_count and not using HAVING:

If you are selecting only a few rows with LIMIT, MySQL uses indexes
in some cases when normally it would prefer to do a full table scan.

If you use LIMIT row_count with ORDER BY, MySQL ends
the sorting as soon as it has found the first row_count lines
rather than sorting the whole table.

When combining LIMIT row_count with DISTINCT, MySQL
stops as soon as it finds row_count unique rows.

In some cases, a GROUP BY can be resolved by reading the key in
order (or doing a sort on the key) and then calculating summaries until the key
value changes. In this case, LIMIT row_count will not calculate
any unnecessary GROUP BY values.

As soon as MySQL has sent the required number of rows to the client, it
aborts the query unless you are using SQL_CALC_FOUND_ROWS.

LIMIT 0 always quickly returns an empty set. This is useful to check
the query or to get the column types of the result columns.

When the server uses temporary tables to resolve the query, the LIMIT
row_count is used to calculate how much space is
required.

6.2.11 How to Avoid Table Scans

The output from EXPLAIN will show ALL in the type
column when MySQL uses a table scan to resolve a query. This usually happens
under the following conditions:

The table is so small that it’s faster to do a table scan than a key lookup.
This is a common case for tables with fewer than 10 rows and a short row
length.

There are no usable restrictions in the ON or WHERE clause
for indexed columns.

You are comparing indexed columns with constant values and MySQL has
calculated (based on the index tree) that the constants cover too large a part
of the table and that a table scan would be faster. See Section 6.2.4, “How
MySQL Optimizes WHERE Clauses.”

You are using a key with low cardinality (many rows match the key value)
through another column. In this case, MySQL assumes that by using the key it
will probably do a lot of key lookups and that a table scan would be
faster.

For small tables, a table scan often is appropriate. For large tables, try
the following techniques to avoid having the optimizer incorrectly choose a
table scan:

Use ANALYZE TABLE tbl_name to update the key distributions
for the scanned table.

Use FORCE INDEX for the scanned table to tell MySQL that table scans
are very expensive compared to using the given index.

Start mysqld with the –max-seeks-for-key=1000 option or
use SET max_seeks_for_key=1000 to tell the optimizer to assume that no
key scan will cause more than 1,000 key seeks. See Section 4.2.3, “Server System
Variables.”

This article is excerpted from MySQL
Administrator’s Guide, by MySQL AB (editor) (Sams, 2004;
ISBN 0672326345). Check it out at your
favorite bookstore today. Buy
this book now.

{mospagebreak title=6.2.12 Speed of INSERT Queries}

The time to insert a record is determined by the following factors, where the
numbers indicate approximate proportions:

Connecting: (3)

Sending query to server: (2)

Parsing query: (2)

Inserting record: (1 x size of record)

Inserting indexes: (1 x number of indexes)

Closing: (1)

This does not take into consideration the initial overhead to open tables,
which is done once for each concurrently running query.

The size of the table slows down the insertion of indexes by log
N, assuming B-tree indexes.

You can use the following methods to speed up inserts:

If you are inserting many rows from the same client at the same time, use
INSERT statements with multiple VALUES lists to insert several
rows at a time. This is much faster (many times faster in some cases) than using
separate single-row INSERT statements. If you are adding data to a
non-empty table, you may tune the bulk_insert_buffer_size variable to
make it even faster. See Section 4.2.3, “Server System Variables.”

If you are inserting a lot of rows from different clients, you can get higher
speed by using the INSERT DELAYED statement.

With MyISAM tables you can insert rows at the same time that
SELECT statements are running if there are no deleted rows in the
tables.

When loading a table from a text file, use LOAD DATA INFILE. This is
usually 20 times faster than using a lot of INSERT statements.

With some extra work, it is possible to make LOAD DATA INFILE run
even faster when the table has many indexes. Use the following procedure:

Optionally create the table with CREATE TABLE.

Execute a FLUSH TABLES statement or a mysqladmin
flush-tables command.

Use myisamchk –keys-used=0 -rq /path/to/db/tbl_name. This
will remove all use of all indexes for the table.

Insert data into the table with LOAD DATA INFILE. This will not
update any indexes and will therefore be very fast.

If you are going to only read the table in the future, use
myisampack to make it smaller. See Section 8.1.3.3, “Compressed Table
Characteristics.”

Re-create the indexes with myisamchk -r -q
/path/to/db/tbl_name. This will create the index tree in memory
before writing it to disk, which is much faster because it avoids lots of disk
seeks. The resulting index tree is also perfectly balanced.

Execute a FLUSH TABLES statement or a mysqladmin
flush-tables command.

Note that LOAD DATA INFILE also performs the preceding optimization
if you insert into an empty MyISAM table; the main difference is that
you can let myisamchk allocate much more temporary memory for the index
creation than you might want the server to allocate for index re-creation when
it executes the LOAD DATA INFILE statement.

As of MySQL 4.0, you can also use ALTER TABLE tbl_name DISABLE
KEYS instead of myisamchk –keys-used=0 -rq
/path/to/db/tbl_name and ALTER TABLE tbl_name ENABLE
KEYS instead of myisamchk -r -q /path/to/db/tbl_name. This
way you can also skip the FLUSH TABLES steps.

You can speed up INSERT operations that are done with multiple
statements by locking your tables:

LOCK TABLES a WRITE;
INSERT INTO a VALUES (1,23),(2,34),(4,33);
INSERT INTO a VALUES (8,26),(6,29);
UNLOCK TABLES;

A performance benefit occurs because the index buffer is flushed to disk only
once after all INSERT statements have completed. Normally there would
be as many index buffer flushes as there are different INSERT
statements. Explicit locking statements are not needed if you can insert all
rows with a single statement.

For transactional tables, you should use BEGIN/COMMIT instead of
LOCK TABLES to get a speedup.

Locking also lowers the total time of multiple-connection tests, although
the maximum wait time for individual connections might go up because they wait
for locks. For example:

If you don’t use locking, connections 2, 3, and 4 will finish before 1 and 5.
If you use locking, connections 2, 3, and 4 probably will not finish before 1 or
5, but the total time should be about 40% faster.

INSERT, UPDATE, and DELETE operations are very
fast in MySQL, but you will obtain better overall performance by adding locks
around everything that does more than about five inserts or updates in a row. If
you do very many inserts in a row, you could do a LOCK TABLES followed
by an UNLOCK TABLES once in a while (about each 1,000 rows) to allow
other threads access to the table. This would still result in a nice performance
gain.

INSERT is still much slower for loading data than LOAD DATA
INFILE, even when using the strategies just outlined.

To get some more speed for MyISAM tables, for both LOAD DATA
INFILE and INSERT, enlarge the key cache by increasing the
key_buffer_size system variable. See Section 6.5.2, “Tuning Server
Parameters.”

6.2.13 Speed of UPDATE Queries

Update queries are optimized as a SELECT query with the additional
overhead of a write. The speed of the write depends on the amount of data being
updated and the number of indexes that are updated. Indexes that are not changed
will not be updated.

Also, another way to get fast updates is to delay updates and then do many
updates in a row later. Doing many updates in a row is much quicker than doing
one at a time if you lock the table.

Note that for a MyISAM table that uses dynamic record format,
updating a record to a longer total length may split the record. If you do this
often, it is very important to use OPTIMIZE TABLE occasionally.

6.2.14 Speed of DELETE Queries

The time to delete individual records is exactly proportional to the number
of indexes. To delete records more quickly, you can increase the size of the key
cache. See Section 6.5.2, “Tuning Server Parameters.”

If you want to delete all rows in the table, use TRUNCATE TABLE
tbl_name rather than DELETE FROM tbl_name.

This article is excerpted from MySQL
Administrator’s Guide, by MySQL AB (editor) (Sams, 2004;
ISBN 0672326345). Check it out at your
favorite bookstore today. Buy
this book now.

{mospagebreak title=6.2.15 Other Optimization Tips}

This section lists a number of miscellaneous tips for improving query
processing speed:

Use persistent connections to the database to avoid connection overhead. If
you can’t use persistent connections and you are initiating many new connections
to the database, you may want to change the value of the
thread_cache_size variable. See Section 6.5.2, “Tuning Server
Parameters.”

Always check whether all your queries really use the indexes you have created
in the tables. In MySQL, you can do this with the EXPLAIN statement.
See Section 6.2.1, “EXPLAIN Syntax (Get Information About a
SELECT).”

Try to avoid complex SELECT queries on MyISAM tables that
are updated frequently, to avoid problems with table locking that occur due to
contention between readers and writers.

With MyISAM tables that have no deleted rows, you can insert rows at
the end at the same time that another query is reading from the table. If this
is important for you, you should consider using the table in ways that avoid
deleting rows. Another possibility is to run OPTIMIZE TABLE after you
have deleted a lot of rows.

Use ALTER TABLE … ORDER BY expr1, expr2, … if you
mostly retrieve rows in expr1, expr2, … order. By using
this option after extensive changes to the table, you may be able to get higher
performance.

In some cases, it may make sense to introduce a column that is “hashed”
based on information from other columns. If this column is short and reasonably
unique, it may be much faster than a big index on many columns. In MySQL, it’s
very easy to use this extra column:

SELECT * FROM tbl_name
WHERE hash_col=MD5(CONCAT(col1,col2))
AND col1=’constant‘ AND col2=’constant‘;

For MyISAM tables that change a lot, you should try to avoid all
variable-length columns (VARCHAR, BLOB, and TEXT).
The table will use dynamic record format if it includes even a single
variable-length column. See Chapter 8, “MySQL Storage Engines and Table
Types.”

It’s normally not useful to split a table into different tables just because
the rows get “big.” To access a row, the biggest performance hit is the disk
seek to find the first byte of the row. After finding the data, most modern
disks can read the whole row fast enough for most applications. The only cases
where it really matters to split up a table is if it’s a MyISAM table
with dynamic record format (see above) that you can change to a fixed record
size, or if you very often need to scan the table but do not need most of the
columns. See Chapter 8, “MySQL Storage Engines and Table Types.”

If you very often need to calculate results such as counts based on
information from a lot of rows, it’s probably much better to introduce a new
table and update the counter in real time. An update of the following form is
very fast:

UPDATE tbl_name SET count_col=count_col+1 WHERE key_col=constant;

This is really important when you use MySQL storage engines such as
MyISAM and ISAM that have only table-level locking (multiple
readers / single writers). This will also give better performance with most
databases, because the row locking manager in this case will have less to
do.

If you need to collect statistics from large log tables, use summary tables
instead of scanning the entire log table. Maintaining the summaries should be
much faster than trying to calculate statistics “live.” It’s much faster to
regenerate new summary tables from the logs when things change (depending on
business decisions) than to have to change the running application!

If possible, you should classify reports as “live” or “statistical,” where
data needed for statistical reports is created only from summary tables that are
generated periodically from the live data.

Take advantage of the fact that columns have default values. Insert values
explicitly only when the value to be inserted differs from the default. This
reduces the parsing that MySQL needs to do and improves the insert speed.

In some cases, it’s convenient to pack and store data into a BLOB
column. In this case, you must add some extra code in your application to pack
and unpack information in the BLOB values, but this may save a lot of
accesses at some stage. This is practical when you have data that doesn’t
conform to a rows-and-columns table structure.

Normally, you should try to keep all data non-redundant (what is called
“third normal form” in database theory). However, do not be afraid to duplicate
information or create summary tables if necessary to gain more speed.

Stored procedures or UDFs (user-defined functions) may be a good way to get
more performance for some tasks. However, if you use a database system that does
not support these capabilities, you should always have another way to perform
the same tasks, even if the alternative method is slower.

You can always gain something by caching queries or answers in your
application and then performing many inserts or updates together. If your
database supports table locks (like MySQL and Oracle), this should help to
ensure that the index cache is only flushed once after all updates.

Use INSERT DELAYED when you do not need to know when your data is
written. This speeds things up because many records can be written with a single
disk write.

Use INSERT LOW_PRIORITY when you want to give SELECT
statements higher priority than your inserts.

Use SELECT HIGH_PRIORITY to get retrievals that jump the queue. That
is, the SELECT is done even if there is another client waiting to do a
write.

Use multiple-row INSERT statements to store many rows with one SQL
statement (many SQL servers support this).

Use LOAD DATA INFILE to load large amounts of data. This is faster
than using INSERT statements.

Use AUTO_INCREMENT columns to generate unique values.

Use OPTIMIZE TABLE once in a while to avoid fragmentation with
MyISAM tables when using a dynamic table format. See Section 8.1.3,
“MyISAM Table Storage Formats.”

Use HEAP tables when possible to get more speed. See Chapter 8,
“MySQL Storage Engines and Table Types.”

When using a normal Web server setup, images should be stored as files. That
is, store only a file reference in the database. The main reason for this is
that a normal Web server is much better at caching files than database contents,
so it’s much easier to get a fast system if you are using files.

Use in-memory tables for non-critical data that is accessed often, such as
information about the last displayed banner for users who don’t have cookies
enabled in their Web browser.

Columns with identical information in different tables should be declared to
have identical data types. Before MySQL 3.23, you get slow joins otherwise.

Try to keep column names simple. For example, in a table named
customer, use a column name of name instead of
customer_name. To make your names portable to other SQL servers, you
should keep them shorter than 18 characters.

If you need really high speed, you should take a look at the low-level
interfaces for data storage that the different SQL servers support! For example,
by accessing the MySQL MyISAM storage engine directly, you could get a
speed increase of two to five times compared to using the SQL interface. To be
able to do this, the data must be on the same server as the application, and
usually it should only be accessed by one process (because external file locking
is really slow). One could eliminate these problems by introducing low-level
MyISAM commands in the MySQL server (this could be one easy way to get
more performance if needed). By carefully designing the database interface, it
should be quite easy to support this type of optimization.

If you are using numerical data, it’s faster in many cases to access
information from a database (using a live connection) than to access a text
file. Information in the database is likely to be stored in a more compact
format than in the text file, so accessing it will involve fewer disk accesses.
You will also save code in your application because you don’t have to parse your
text files to find line and column boundaries.

Replication can provide a performance benefit for some operations. You can
distribute client retrievals among replication servers to split up the load. To
avoid slowing down the master while making backups, you can make backups using a
slave server. See Chapter 5, “Replication in MySQL.”

Declaring a MyISAM table with the DELAY_KEY_WRITE=1 table
option makes index updates faster because they are not flushed to disk until the
table is closed. The downside is that if something kills the server while such
tables are open, you should ensure that they are okay by running the server with
the –myisam-recover option, or by running myisamchk before
restarting the server. (However, even in this case, you should not lose anything
by using DELAY_KEY_WRITE, because the key information can always be
generated from the data rows.)

This article is excerpted from MySQL
Administrator’s Guide, by MySQL AB (editor) (Sams, 2004;
ISBN 0672326345). Check it out at your
favorite bookstore today. Buy
this book now.